1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
27/7/2024 Comida 27300 Andrés NA
28/7/2024 Comida 67250 Tami Supermercado
30/7/2024 Comida 30650 Andrés piwén
1/8/2024 Electricidad 209425 Andrés NA
3/8/2024 Comida 45130 Tami Supermercado
3/8/2024 Comida 19490 Tami Barritas Wild Soul
11/8/2024 Comida 8230 Tami Supermercado
8/8/2024 Comida 5000 Andrés platano y cosas
15/8/2024 Electricidad 190084 Andrés enel rqlo (dupliqué el gasto para que pagues eso. Yo ya ni uso mi estufa, y tal vez tú deberías usar la mía porque tiene temporizador)
18/8/2024 VTR 21990 Andrés NA
16/8/2024 Comida 11756 Andrés lider
19/8/2024 Comida 91135 Tami Supermercado
23/8/2024 Otros 170000 Andrés instalacion
23/8/2024 Otros 285000 Andrés aire
25/8/2024 Gas 75000 Andrés NA
25/8/2024 Comida 66781 Tami Supermercado
29/8/2024 Comida 45000 Andrés piwen
2/9/2024 Comida 42057 Tami Supermercado
4/9/2024 Agua 11723 Andrés NA
4/9/2024 Comida 19490 Tami Barritas Wild Soul
7/9/2024 Comida 15960 Tami Supermercado
8/9/2024 Gas/Bencina 20182 Tami Parafina
8/9/2024 Comida 55162 Tami Supermercado
14/9/2024 Comida 16000 Andrés NA
14/9/2024 Comida 59823 Tami Supermercado
20/9/2024 Electricidad 22000 Andrés NA
23/9/2024 Comida 60298 Tami Supermercado
26/9/2024 Rgalo Chepa 31036 Tami NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 8.8632e+08   2    6.8387 0.0011 ** 
## lag_depvar    1.5071e+11   1 2325.6945 <2e-16 ***
## Residuals     4.9185e+10 759                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff          lwr      upr     p adj
## 1-0  7228.838    -8.325119 14466.00 0.0503408
## 2-0 30464.943 23932.015922 36997.87 0.0000000
## 2-1 23236.105 19431.319722 27040.89 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
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## 708  98395.14             2   95424.29
## 709 115594.71             2   98395.14
## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   607 52699.21 17634.641
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1988.46987   4027.33828   -525.09915   2449.31775  -2943.97896    528.07012 
##            8            9           10           11           12           13 
##  -5642.13918  -1203.17598  -3983.10388   -451.10514  -4968.11280  -1657.69791 
##           14           15           16           17           18           19 
##   -947.71286    333.58910  -3276.39793   -421.62760  -2167.50344   6562.94093 
##           20           21           22           23           24           25 
##  -1527.44492  -1212.13894   1468.59074  -1182.14618    235.00059   1699.33814 
##           26           27           28           29           30           31 
##  -7086.45630    926.32409   8181.78765    455.42373     23.67100  -2364.83413 
##           32           33           34           35           36           37 
##   1597.58539   4602.47943   1180.34605   2447.06236  -1803.74383   4657.08392 
##           38           39           40           41           42           43 
##   4332.74978  -2226.93047  -2950.63822  -1098.81786 -10737.20313   7236.21780 
##           44           45           46           47           48           49 
##   2549.76858   1374.12145   8119.02340    741.58281   6581.09983   6795.08807 
##           50           51           52           53           54           55 
##  -5775.87310  -4733.39520  -5030.72658  -7929.84574   6086.82295  -4081.38014 
##           56           57           58           59           60           61 
##  -4919.92107   3807.66180    866.45383    -45.86821    130.25406  -5005.91854 
##           62           63           64           65           66           67 
##  18092.17368   3706.94804  -3568.57543   5974.04366   7418.38374  14743.22614 
##           68           69           70           71           72           73 
##   1862.55631 -13055.32812  -1238.33887   4696.20385  -4829.23825  -4368.08705 
##           74           75           76           77           78           79 
## -10488.56936   2419.49899  -5427.60511   1011.30246  -6906.07573    476.24087 
##           80           81           82           83           84           85 
##  -2413.05186  -2757.01470  -4000.89228   -618.19075   2241.65750   3711.44922 
##           86           87           88           89           90           91 
##    452.22109   -503.39637    177.78798   4286.73599  -1153.38572   1153.35181 
##           92           93           94           95           96           97 
##  -2055.98132  -1047.52725    169.49123    268.88889  -7487.50099   2349.61608 
##           98           99          100          101          102          103 
##  -8626.40080  -3006.30578  -4111.86145  -1820.68965  -1343.29710   3103.20602 
##          104          105          106          107          108          109 
##  -2392.90227   2537.61431  -1193.75657    933.78145   2560.42898  -3163.82345 
##          110          111          112          113          114          115 
##  -4747.58788   -896.19536   1859.24960  11665.16974  -1205.67521   2694.53088 
##          116          117          118          119          120          121 
##   4299.88805   3557.78338  -1033.03830  -4663.30782  -3702.20220   2319.68635 
##          122          123          124          125          126          127 
##  -1720.18009   1342.56222   8867.50339    902.09879    183.29734  -2474.06259 
##          128          129          130          131          132          133 
##   2683.39673   7091.57035   1083.97429  -8431.24892   1764.37487   4158.27011 
##          134          135          136          137          138          139 
##  -3122.02778  -1399.43125   -843.39626  -3874.94005   1167.39838   -502.61909 
##          140          141          142          143          144          145 
##  -2922.14202   1695.69780  -1891.38572  -7847.90026   1982.36360  -3518.61041 
##          146          147          148          149          150          151 
##   2050.21278   -291.65573    992.02900   -380.79599   1331.74524   1176.08768 
##          152          153          154          155          156          157 
##   3353.81981  -4846.33865  -1186.25966  -3252.02478   5925.81151   9751.16594 
##          158          159          160          161          162          163 
##  -3596.29490  -4952.55613   3414.21382     35.22149   2545.03569  -6041.52060 
##          164          165          166          167          168          169 
##  -6905.50866   3970.75942  17238.27593   3562.09231   -452.74779  -2509.67534 
##          170          171          172          173          174          175 
##  -1186.04887   3499.21747   -304.96454  -8158.10099   2737.61253   4214.62724 
##          176          177          178          179          180          181 
##    534.94830   8659.53703  -9300.59615  -3576.78055 -10867.97405 -11414.62777 
##          182          183          184          185          186          187 
##   1013.38306   9091.00884  -1577.08271   5778.06021   6435.95500  13067.58297 
##          188          189          190          191          192          193 
##   8394.02806  -4076.31068   2414.17281  10315.42291  -1663.51756  -2489.96853 
##          194          195          196          197          198          199 
## -10351.39343  -6490.94392   1075.01716  -5383.94535  -9968.55362   5172.17782 
##          200          201          202          203          204          205 
##  -3244.76287  -1896.78360   -989.26109   6312.43259   9733.20142    471.10579 
##          206          207          208          209          210          211 
##   2813.79869   2994.34140   5687.92797  12755.12544  -5719.95630 -11368.59208 
##          212          213          214          215          216          217 
##  -5797.00441 -10743.53715  -5274.23317   1314.83277 -13205.06308  16147.35261 
##          218          219          220          221          222          223 
##   7651.75194   1405.50065  26568.92981  12514.16082   7355.41055  14053.70605 
##          224          225          226          227          228          229 
##  -3852.68979  -1725.79274   3762.68422    343.48931   2714.38942   8970.64279 
##          230          231          232          233          234          235 
##   5822.59385  -1903.68701  -1851.01788   9380.35787 -11525.33172  -7365.94876 
##          236          237          238          239          240          241 
##  -8662.55150 -10261.93124   2876.80970   1173.30611  -8464.18702  -9187.70192 
##          242          243          244          245          246          247 
##   8866.93790  -7942.08190   2283.21236 -10485.55853  -4275.37475   1195.60270 
##          248          249          250          251          252          253 
##    793.92141 -12511.43612   3403.76052   1851.43940   4018.46245   1965.29440 
##          254          255          256          257          258          259 
##  -1318.23577  10977.98079  20765.74027   3157.50715  -4316.03742   4031.09207 
##          260          261          262          263          264          265 
##  -1768.35624   3644.53478  -4938.99857 -11010.17461  -4894.08673   -704.58867 
##          266          267          268          269          270          271 
##  -5369.50630   8579.64941  -4440.11056   4012.22023  -2266.65274   4261.93661 
##          272          273          274          275          276          277 
##    556.83457   7150.80661  -1540.14258  11884.86645  -4686.86933   1593.81382 
##          278          279          280          281          282          283 
##   -504.86707   7710.56445  -5176.12682  -2876.11093 -11419.45396  -2867.15452 
##          284          285          286          287          288          289 
##  18453.02196   7650.34988   2620.78350   -742.46019    780.37730   6267.76174 
##          290          291          292          293          294          295 
##   6765.95505 -18875.50387 -11311.78881  -8326.48276   9443.17659   2892.15865 
##          296          297          298          299          300          301 
##  -1345.30845  27233.79017   9982.48252   4835.55507   9451.73629   2803.66033 
##          302          303          304          305          306          307 
##  -1094.26974   7815.47089 -24365.42363  -3688.56726   -337.75352  -7127.94010 
##          308          309          310          311          312          313 
##  -4148.23363   2751.13785  -9357.21259  -3415.07819  -8370.08807   1369.40486 
##          314          315          316          317          318          319 
##  -3330.53581   1869.41768  -4247.31928  27275.09754   -834.35837   3170.64317 
##          320          321          322          323          324          325 
##  10712.09908   5492.77948  32288.87634   5096.43836 -20958.54404   1696.29737 
##          326          327          328          329          330          331 
##   1011.11484  -6568.78566  -1861.01517 -33400.91297    699.41434  -2463.83179 
##          332          333          334          335          336          337 
##   -245.12120  -3306.30778   3950.78796   -551.83164  -7061.54802  -3235.68481 
##          338          339          340          341          342          343 
##  -2309.61206  -7794.12294   3727.33893  -1477.79933  -1841.54231  -1095.93699 
##          344          345          346          347          348          349 
##     77.71024    387.74031  -1708.08824  -9537.74390 -13320.28303   2176.08698 
##          350          351          352          353          354          355 
##  -4441.79459  -3778.61788  -6099.59217   1626.44577   1272.09324   2649.03665 
##          356          357          358          359          360          361 
##  -3862.13800   -618.79976    576.13372   6915.64266    193.04971   -123.52650 
##          362          363          364          365          366          367 
##   2495.67729  -2834.23687   -967.93149  -8835.08766  -4731.41035  -6320.66860 
##          368          369          370          371          372          373 
##  -5062.38774  -7366.65156   4895.83833    267.66699   7020.80849  -7720.57372 
##          374          375          376          377          378          379 
##  -2359.58156  -3484.34337  -2564.87903 -12554.61568   1789.14688 -10736.78415 
##          380          381          382          383          384          385 
##   5580.91306   9237.47860   3045.12058  -2476.49129   1520.25575   6660.53788 
##          386          387          388          389          390          391 
##  11334.55170  -5868.06667  -5450.77986   -261.20944   8456.39387   1718.16785 
##          392          393          394          395          396          397 
##  11121.35499  -9969.17106   2656.57676    595.07849    441.59728   -777.35783 
##          398          399          400          401          402          403 
##   -690.62301 -14617.55591   8376.71328  -1304.15786  -1493.71944   6862.14501 
##          404          405          406          407          408          409 
##  -8039.71712  -1413.37075  -2644.03009  -5928.83591  -2969.33584  -4023.15096 
##          410          411          412          413          414          415 
##  -8858.93364   6025.76630   1547.91599  -7460.71959  -7786.30288  14122.13237 
##          416          417          418          419          420          421 
##   3736.78050   4412.07166  -8115.92389  -4837.04806  -2697.04409   2725.14071 
##          422          423          424          425          426          427 
## -14098.44038  -2893.60409  -9196.78280   2910.82932   6886.15272   6496.03709 
##          428          429          430          431          432          433 
##  -4059.66326  -4199.26862  -4804.66990  -1875.38117  -5796.34989  -6714.95483 
##          434          435          436          437          438          439 
##  -6041.78113  -1487.30042   -937.26619  -5060.39197   2494.33396   4759.90157 
##          440          441          442          443          444          445 
##  -5128.12011  -2235.55439   1498.48832  -3910.80552   2758.22334  -6649.84487 
##          446          447          448          449          450          451 
## -12189.97026  -4604.19022   9553.00872  -2103.80257   4682.27376  -5932.44935 
##          452          453          454          455          456          457 
##  -1193.14631    314.32921   2959.62015 -12329.27332   3290.23884  -6770.87156 
##          458          459          460          461          462          463 
##   6444.82331   2948.56973   2448.56971  -3901.07409   2030.48366    -66.26236 
##          464          465          466          467          468          469 
##   1733.35122   -578.88751   3290.65163  -2694.92634   5744.16099  -6994.27948 
##          470          471          472          473          474          475 
##  -3027.56361  -2270.65644  -4729.27814   2927.16332   7736.40720  -6064.46636 
##          476          477          478          479          480          481 
##   1426.72661  -6232.53207  -2906.73753   1948.18213 -12987.21433  -9832.36842 
##          482          483          484          485          486          487 
##  -1290.54975    -62.09947  -1037.71674  -1413.49707  -9653.86293  11013.73746 
##          488          489          490          491          492          493 
##   6179.36100   7376.34094  -5469.36924   5321.92526   9255.37361   6028.41892 
##          494          495          496          497          498          499 
## -13495.01816 -10615.96168  -3508.43141  -1174.64453   -590.29298  -7688.52068 
##          500          501          502          503          504          505 
##    539.66736   4223.52943   5456.47365    619.61829     40.55657  -7279.37679 
##          506          507          508          509          510          511 
##    516.85383  -5097.92728   1775.99579  -1346.49092  -8208.22808   -664.91301 
##          512          513          514          515          516          517 
##  -2733.84422   -647.97253   1274.97966  -9546.59179  -7831.61945  24210.85195 
##          518          519          520          521          522          523 
##   9799.46834   5867.10251  -5344.61230   2772.03738  16992.89545  11471.47471 
##          524          525          526          527          528          529 
## -24142.20785  -5107.82116  -3791.37300   4508.46054   -410.71312 -11157.57374 
##          530          531          532          533          534          535 
##   4317.49123  13847.68344  -5013.10667   4324.36965   5511.23327  -1827.95499 
##          536          537          538          539          540          541 
##  -4586.06447  -7129.42913  -2167.68251   8255.05032     77.46133  -8191.52497 
##          542          543          544          545          546          547 
##   1751.59330   -657.78608    308.84436 -11085.88800 -11134.49214   1953.63359 
##          548          549          550          551          552          553 
##   6928.29240  -1374.43543    780.29707  -7773.28154   8500.58103    861.59894 
##          554          555          556          557          558          559 
## -11987.68917   9097.10370   8611.71785     66.79381   4816.79958  -3605.94856 
##          560          561          562          563          564          565 
##  14065.80536  21477.13471  -6459.79686  -9699.50418   6730.64947    189.52261 
##          566          567          568          569          570          571 
##   3412.84768  -7417.44123 -17364.43856   6578.33819   6369.72994   1852.34590 
##          572          573          574          575          576          577 
##   3052.33200   1729.37639  -2203.54090  14673.18100  -9668.48882  -6288.31747 
##          578          579          580          581          582          583 
##   8654.81983   2818.42998  -6582.67569   7457.53042  -3837.47003  -2822.18084 
##          584          585          586          587          588          589 
##  15650.93641 -14522.44068   8373.11193     31.85720  -6252.29292   -805.83972 
##          590          591          592          593          594          595 
##    201.02812 -10701.57588   1731.18994  -7201.08096   3002.15819   8812.50014 
##          596          597          598          599          600          601 
##  -7534.79153   5802.97034   2699.49526   6825.02207  -3209.60588   6120.83937 
##          602          603          604          605          606          607 
##  -8317.21321   2218.03024   1237.50529   3107.90005   1470.09111    373.61675 
##          608          609          610          611          612          613 
##  -5835.20377   8037.89760  -1204.03412  -2598.23731  -3482.76680  -8263.43498 
##          614          615          616          617          618          619 
##  11909.76760   4915.55063  -9326.32605  11594.06200   6042.35563  -5571.18080 
##          620          621          622          623          624          625 
##  26346.73473 -12784.18177  -6773.19004   3146.38652  -4164.35553 -10607.87233 
##          626          627          628          629          630          631 
##  11258.65550 -21645.52755  -2473.26353   8618.98985  11105.53616  -1555.46764 
##          632          633          634          635          636          637 
##  33276.14345  -6522.45045   5754.35093   5440.51624  -2224.39075  -5315.53829 
##          638          639          640          641          642          643 
##  -1929.42063 -12428.74380  -2270.93439  -1917.46806  -2552.83338  -2892.93624 
##          644          645          646          647          648          649 
##   1777.41868   4401.65394  16948.47199  18575.54563    941.08213   4835.78191 
##          650          651          652          653          654          655 
##  10658.49274  20211.30534    843.85630 -27977.71323  -1335.54542  -2288.41967 
##          656          657          658          659          660          661 
##   1866.85353  -3186.26693 -10622.53571   1639.08238   4211.15711  -1006.54146 
##          662          663          664          665          666          667 
##  13031.76008   1396.40408   1849.42936 -11653.86081   1380.65810   1194.80650 
##          668          669          670          671          672          673 
##  -5153.65346  -7410.77718   2049.02581  -3717.14808   2661.67061  -3377.93358 
##          674          675          676          677          678          679 
##  -9342.28382  -8336.20216  -3027.76460    121.57411   2805.23498    688.00632 
##          680          681          682          683          684          685 
##  -3843.70921  -1832.69757  -1342.43408  -8265.04813   4605.46910  -2262.81864 
##          686          687          688          689          690          691 
##  -1420.58399    566.16202  10839.57895   9874.70038  10680.18252  -9574.88509 
##          692          693          694          695          696          697 
##  -3503.31171  -3102.90479   5896.81872 -10339.61410  -7901.16659  -8624.16869 
##          698          699          700          701          702          703 
##  -6310.71816  -4788.70028   3026.14456  -4436.52368  -1940.09854   4182.69098 
##          704          705          706          707          708          709 
##  31091.02850   9646.30945  23609.74201   1950.16330   8580.58513  23199.82472 
##          710          711          712          713          714          715 
##   6934.02961 -17827.56892   5077.80186  -5182.65084    112.55459    676.87014 
##          716          717          718          719          720          721 
## -17080.89611  -5177.64709   3394.91977  -2933.32428 -12912.50322   4283.31834 
##          722          723          724          725          726          727 
##  -5520.84376    756.36699  -3908.08692 -12432.95876   1328.58644  -1883.64528 
##          728          729          730          731          732          733 
##  -9790.70601  17219.18079   1828.60545  -2656.61573   5770.11343  -8543.27166 
##          734          735          736          737          738          739 
##   -679.44321   8183.87029 -15260.28894  -5897.24595   7401.29021  -4742.94076 
##          740          741          742          743          744          745 
##    183.86230   1859.24556  -1908.57386  -5125.87363   6434.30436  -6213.36484 
##          746          747          748          749          750          751 
##  22731.88086   7980.94907  -1762.07928  -7125.36758  23533.14207  -4058.99378 
##          752          753          754          755          756          757 
##   1589.46323 -14234.59032  56211.20043  27313.66572  15594.97213 -10114.10038 
##          758          759          760          761          762          763 
##  11032.40608   7756.78648   6249.12134 -45959.70145 -16023.64424   1024.00437 
##          764 
##  -2448.57368 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17280.82  20111.66  24341.24  24060.83  26400.69  23748.64  24460.85  19720.32 
##        10        11        12        13        14        15        16        17 
##  19458.39  16816.39  17589.40  14337.56  14388.43  15049.27  16736.11  15065.77 
##        18        19        20        21        22        23        24        25 
##  16094.50  15471.63  22513.44  21602.71  21085.55  22964.72  22294.57  22943.38 
##        26        27        28        29        30        31        32        33 
##  24778.74  18741.96  20458.21  28250.58  28307.90  27982.69  25625.70  27020.09 
##        34        35        36        37        38        39        40        41 
##  30841.08  31187.51  32588.60  30113.49  34110.25  37299.93  34372.92  31202.10 
##        42        43        44        45        46        47        48        49 
##  30056.49  20690.07  28165.66  30588.16  31671.12  38469.99  37967.47  42602.91 
##        50        51        52        53        54        55        56        57 
##  46814.87  39554.68  34154.30  29205.56  22389.32  28643.24  25243.49  21562.34 
##        58        59        60        61        62        63        64        65 
##  25945.40  27197.73  27493.03  27902.49  23797.11  40293.19  42126.58  37399.81 
##        66        67        68        69        70        71        72        73 
##  41582.62  46470.06  57077.02  55102.19  40430.05  37950.22  40950.81  35283.66 
##        74        75        76        77        78        79        80        81 
##  30762.00  21518.79  24701.89  20650.98  22725.08  17649.90  19653.77  18884.73 
##        82        83        84        85        86        87        88        89 
##  17918.04  15998.05  17268.49  20855.84  25248.21  26232.40  26257.21  26870.41 
##        90        91        92        93        94        95        96        97 
##  30971.81  29809.08  30802.70  28878.24  28082.65  28448.68  28852.93  22467.24 
##        98        99       100       101       102       103       104       105 
##  25464.97  18535.45  17398.15  15450.12  15748.15  16421.65  20868.62  19957.39 
##       106       107       108       109       110       111       112       113 
##  23448.33  23239.50  24906.00  27766.25  25278.73  21742.62  22016.46  24647.54 
##       114       115       116       117       118       119       120       121 
##  35449.68  33652.90  35479.83  38460.93  40405.61  38107.31  32958.06  29320.46 
##       122       123       124       125       126       127       128       129 
##  31391.32  29681.15  30855.93  38412.04  38056.56  37123.49  34005.03  35776.00 
##       130       131       132       133       134       135       136       137 
##  41142.88  40586.39  31838.63  33096.16  36267.60  32698.86  31095.40  30185.65 
##       138       139       140       141       142       143       144       145 
##  26762.46  28168.76  27939.71  25639.30  27652.10  26284.76  19923.64  22936.75 
##       146       147       148       149       150       151       152       153 
##  20775.93  23735.94  24272.83  25854.08  26035.11  27679.77  28973.04  31987.77 
##       154       155       156       157       158       159       160       161 
##  27483.97  26751.17  24320.47  30180.69  41616.72  39956.56  37336.64  42328.06 
##       162       163       164       165       166       167       168       169 
##  43728.54  47124.81  42616.79  37950.95  43345.01  59553.48  61752.89  60176.10 
##       170       171       172       173       174       175       176       177 
##  57020.05  55428.50  58115.54  57145.24  49481.67  52288.94  56010.05  56046.03 
##       178       179       180       181       182       183       184       185 
##  63133.88  53690.78  50460.40  41321.91  32909.90  36397.99  46443.37  45902.51 
##       186       187       188       189       190       191       192       193 
##  51821.05  57532.99  68253.97  73506.45  67237.40  67429.72  74459.37  70160.68 
##       194       195       196       197       198       199       200       201 
##  65709.25  55014.94  49079.41  50495.52  46115.55  38329.39  44717.19  42954.78 
##       202       203       204       205       206       207       208       209 
##  42594.83  43070.42  49825.37  58663.47  58295.20  60010.09  61656.36  65425.73 
##       210       211       212       213       214       215       216       217 
##  74837.81  66966.16  55223.15  49862.97  40911.09  37886.31  40982.06  31059.65 
##       218       219       220       221       222       223       224       225 
##  47935.53  55214.21  56110.93  78745.41  86197.30  88189.01  95736.69  86739.65 
##       226       227       228       229       230       231       232       233 
##  80772.60  80356.94  77026.18  76192.50  80902.26  82258.69  76726.16  71966.64 
##       234       235       236       237       238       239       240       241 
##  77587.76  64312.38  56394.69  48391.65  40051.48  44219.27  46359.62  39847.99 
##       242       243       244       245       246       247       248       249 
##  33563.92  43787.22  38067.22  41980.27  34288.66  33001.97  36636.22  39443.86 
##       250       251       252       253       254       255       256       257 
##  30326.10  36229.99  40009.54  45174.42  47877.09  47372.59  57614.26  75010.78 
##       258       259       260       261       262       263       264       265 
##  74826.89  68176.05  69649.36  65891.89  67329.71  61123.32  50459.66  46509.87 
##       266       267       268       269       270       271       272       273 
##  46718.08  42847.21  51600.68  47895.21  52018.08  50145.49  54189.45  54483.76 
##       274       275       276       277       278       279       280       281 
##  60466.57  58114.42  67731.73  61691.47  61900.30  60258.86  65968.70  59735.25 
##       282       283       284       285       286       287       288       289 
##  56318.88  45931.30  44337.26  61470.36  66968.65  67375.75  64808.19  63900.81 
##       290       291       292       293       294       295       296       297 
##  67878.76  71766.50  52872.36  43031.34  37076.82  47338.84  50562.02  49681.07 
##       298       299       300       301       302       303       304       305 
##  73738.23  79649.44  80313.26  84899.20  83108.13  78166.96  81613.85  56657.00 
##       306       307       308       309       310       311       312       313 
##  52939.61  52621.23  46447.09  43672.58  47255.21  39850.22  38579.66  33172.45 
##       314       315       316       317       318       319       320       321 
##  36935.25  36121.30  39930.75  37926.76  63564.93  61418.50  63032.76  70984.93 
##       322       323       324       325       326       327       328       329 
##  73358.55  98693.85  97080.83  73049.85  71854.60  70221.36  62219.30  59358.06 
##       330       331       332       333       334       335       336       337 
##  29479.01  33145.40  33582.41  35889.02  35233.64  40967.55  42036.98  37311.83 
##       338       339       340       341       342       343       344       345 
##  36530.75  36656.69  32002.52  37967.09  38626.69  38883.65  39754.43  41530.12 
##       346       347       348       349       350       351       352       353 
##  43341.66  43094.74  36079.85  26701.77  32015.79  30883.33  30475.74  28105.84 
##       354       355       356       357       358       359       360       361 
##  32757.91  36490.68  40928.71  39128.09  40381.15  42507.36  49860.24  50407.67 
##       362       363       364       365       366       367       368       369 
##  50608.18  53057.24  50555.07  50002.80  42690.12  39902.95  36101.82  33893.22 
##       370       371       372       373       374       375       376       377 
##  29973.59  37219.76  39493.62  47334.00  41340.15  40790.49  39336.16  38871.62 
##       378       379       380       381       382       383       384       385 
##  29791.57  34363.36  27454.80  35627.09  45901.02  49446.06  47729.32  49709.60 
##       386       387       388       389       390       391       392       393 
##  55894.16  65325.35  58575.49  53075.35  52805.61  60142.98  60663.36  69282.46 
##       394       395       396       397       398       399       400       401 
##  58450.42  60008.35  59570.97  59057.79  57553.34  56321.98  43156.29  51692.87 
##       402       403       404       405       406       407       408       409 
##  50699.01  49671.14  56035.86  48620.94  47936.03  46272.26  41974.19  40811.58 
##       410       411       412       413       414       415       416       417 
##  38886.51  33014.38  40842.23  43751.86  38454.59  33570.87  48357.65  52180.50 
##       418       419       420       421       422       423       424       425 
##  56087.35  48599.48  44943.76  43627.29  47193.30  35678.46  35409.21  29700.74 
##       426       427       428       429       430       431       432       433 
##  35258.70  43538.82  50391.66  47175.55  44260.96  41203.67  41092.49  37590.38 
##       434       435       436       437       438       439       440       441 
##  33750.78  31000.59  32567.69  34406.53  32422.52  37260.96  43431.12  40201.98 
##       442       443       444       445       446       447       448       449 
##  39909.65  42898.95  40797.06  44763.84  40037.83  31121.19  29965.28  41257.52 
##       450       451       452       453       454       455       456       457 
##  40940.87  46559.88  42220.86  42568.53  44179.81  47876.84  37808.76  42630.44 
##       458       459       460       461       462       463       464       465 
##  38079.75  45605.72  49105.72  51711.36  48459.52  50786.98  50987.36  52724.46 
##       466       467       468       469       470       471       472       473 
##  52224.92  55151.93  52495.41  57517.85  50816.14  48440.66  47034.85  43678.41 
##       474       475       476       477       478       479       480       481 
##  47413.16  54834.04  49292.70  50986.25  45804.74  44192.96  47009.79  36484.23 
##       482       483       484       485       486       487       488       489 
##  30082.41  31941.10  34622.43  36103.93  37064.29  30741.26  43200.21  49822.52 
##       490       491       492       493       494       495       496       497 
##  56613.94  51355.50  56161.05  63751.30  67541.02  53875.53  44507.00  42543.22 
##       498       499       500       501       502       503       504       505 
##  42864.58  43651.23  38169.33  40554.61  45825.95  51475.24  52180.87  52290.81 
##       506       507       508       509       510       511       512       513 
##  46028.57  47360.93  43641.43  46381.21  46048.80  39800.34  40924.99  40104.83 
##       514       515       516       517       518       519       520       521 
##  41204.16  43829.16  36710.05  32016.29  55769.96  63884.18  67516.33  60933.11 
##       522       523       524       525       526       527       528       529 
##  62264.96  75773.24  82710.21  57803.11  52702.37  49415.54  53769.57  53278.72 
##       530       531       532       533       534       535       536       537 
##  43518.22  48481.60  61069.96  55622.06  59000.34  62965.38  60034.78  55093.86 
##       538       539       540       541       542       543       544       545 
##  48593.40  47256.95  55148.82  54900.67  47503.12  49714.07  49541.73  50231.60 
##       546       547       548       549       550       551       552       553 
##  40933.92  32816.22  37133.28  45203.58  45001.70  46697.85  40741.85  49703.40 
##       554       555       556       557       558       559       560       561 
##  50852.12  40689.61  50176.14  57994.06  57362.63  60939.81  56731.19  68424.58 
##       562       563       564       565       566       567       568       569 
##  85017.94  75165.50  63794.35  68188.33  66323.44  67503.30  59121.44  43201.95 
##       570       571       572       573       574       575       576       577 
##  50170.56  56041.94  57217.95  59281.62  59924.97  57067.82  69244.49  58678.60 
##       578       579       580       581       582       583       584       585 
##  52437.47  59995.57  61490.96  54624.47  60855.18  56456.61  53518.06  67010.58 
##       586       587       588       589       590       591       592       593 
##  52522.46  59824.71  58922.29  52680.41  51989.54  52264.00  43032.95  45813.80 
##       594       595       596       597       598       599       600       601 
##  40470.98  44692.50  53405.65  46775.03  52600.50  54964.69  60601.32  56781.45 
##       602       603       604       605       606       607       608       609 
##  61567.64  53184.54  55053.78  55825.67  58120.62  58691.38  58234.78  52445.53 
##       610       611       612       613       614       615       616       617 
##  59466.75  57537.95  54651.77  51376.72  44379.95  55824.31  59689.47  50676.80 
##       618       619       620       621       622       623       624       625 
##  61019.22  65180.18  58707.27  80807.47  66015.48  58388.76  60380.21  55760.16 
##       626       627       628       629       630       631       632       633 
##  46150.92  56796.96  37464.69  37325.72  46839.18  57261.75  55317.57  83881.88 
##       634       635       636       637       638       639       640       641 
##  74124.36  76312.48  77940.39  72696.97  65457.99  62111.60  50085.93  48463.61 
##       642       643       644       645       646       647       648       649 
##  47361.55  45852.51  44246.44  46907.92  51498.81  66383.74  80725.20  77865.08 
##       650       651       652       653       654       655       656       657 
##  78763.65  84601.41  97968.86  92757.57  63198.40  60664.85  57636.72  58615.70 
##       658       659       660       661       662       663       664       665 
##  55077.11  45544.92  47915.56  52208.54  51405.38  62900.74  62779.14  63067.00 
##       666       667       668       669       670       671       672       673 
##  51588.77  52940.48  53953.08  49318.63  43332.97  46350.43  43963.04  47429.79 
##       674       675       676       677       678       679       680       681 
##  45195.14  38073.92  32762.62  32760.14  35493.34  40198.14  42445.57  40461.55 
##       682       683       684       685       686       687       688       689 
##  40485.01  40931.19  35306.10  41599.10  41099.44  41396.98  43380.99  54027.16 
##       690       691       692       693       694       695       696       697 
##  62435.82  70438.74  59797.17  55827.90  52728.18  57852.61  48201.31  41936.60 
##       698       699       700       701       702       703       704       705 
##  35867.43  32605.41  31094.14  36569.10  34842.67  35511.45  41410.26  69904.83 
##       706       707       708       709       710       711       712       713 
##  76027.97  93474.12  89814.56  92394.89 107333.54 106180.85  83673.06  84018.37 
##       714       715       716       717       718       719       720       721 
##  75406.59  72525.99  70514.18  53343.36  48768.22  52240.18  49759.36  38937.25 
##       722       723       724       725       726       727       728       729 
##  44473.13  40765.92  42998.09  40885.53  31646.41  35574.36  36195.99  29868.25 
##       730       731       732       733       734       735       736       737 
##  47831.68  50066.33  48111.60  53732.84  46183.30  46456.27  54391.57  40921.39 
##       738       739       740       741       742       743       744       745 
##  37354.14  45806.23  42599.42  44093.33  46846.00  45964.30  42404.12  49352.51 
##       746       747       748       749       750       751       752       753 
##  44402.40  65243.34  70532.79  66664.65  58646.72  78311.14  71425.54  70351.02 
##       754       755       756       757       758       759       760       761 
##  55673.80 104111.48 121083.03 125645.39 107278.45 109692.64 108944.45 106985.13 
##       762       763       764 
##  59937.50  45075.28  46973.43 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8226
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original    bias    std. error
## t1*    6.838734  0.493911    3.179623
## t2* 2325.694463 33.476623  339.713445
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    2.827543       6.965058   13.07406
## 2    lag_depvar 1842.531729    2340.533064 2954.02149

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Sep 30 00:49:00 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Sep 30 00:49:08 2024
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## =-=-=-=-= Iteration 4000 Mon Sep 30 00:49:15 2024
##  =-=-=-=-=
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##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Sep 30 00:49:38 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Sep 30 00:49:45 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Sep 30 00:49:53 2024
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## =-=-=-=-= Iteration 16000 Mon Sep 30 00:50:00 2024
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## =-=-=-=-= Iteration 18000 Mon Sep 30 00:50:08 2024
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_24 %>% 
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))
Item 2024 2023 2022 2021 2020
Agua 4.081250 5.195333 5.410333 5.849167 6.334807
Comida 332.209000 366.009167 312.386750 317.896583 343.810105
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.000000
Electricidad 96.293625 38.104750 47.072333 29.523000 42.386053
Enceres 34.359750 18.259750 24.219750 14.801167 25.094561
Farmacia 0.000000 10.704083 2.835000 13.996083 8.751983
Gas/Bencina 62.041250 42.636000 45.575000 13.583667 34.125403
Diosi 38.008625 55.804250 31.180667 52.687833 42.554351
donaciones/regalos 0.000000 0.000000 0.000000 0.000000 0.000000
Electrodomésticos/ Mantención casa 0.000000 0.000000 0.000000 0.000000 0.000000
VTR 21.991250 12.829167 25.156667 19.086917 19.125754
Netflix 2.087125 8.713833 7.151583 7.028750 6.849018
Otros 102.375000 5.481667 5.000000 0.000000 16.575088
Total 693.446875 563.738000 505.988083 474.453167 545.607123
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")

tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  47 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2474, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2024-10-09 00:04:58 sería de: 38.249 pesos// Percentil 95% más alto proyectado: 41.264,1

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 37947.23 37946.67
Lo.80 37952.71 37952.01
Point.Forecast 38249.38 39073.92
Hi.80 39961.78 43859.49
Hi.95 40899.08 46392.82


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(3,1,0) 
## 
## Coefficients:
##           ar1      ar2      ar3
##       -0.8424  -0.7260  -0.4724
## s.e.   0.1183   0.1313   0.1236
## 
## sigma^2 = 35220:  log likelihood = -438.29
## AIC=884.59   AICc=885.24   BIC=893.35
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(0,0,0) errors 
## 
## Coefficients:
##       intercept     xreg
##        471.3438  17.8651
## s.e.   204.5646   6.3520
## 
## sigma^2 = 34928:  log likelihood = -444.5
## AIC=895   AICc=895.38   BIC=901.61
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 835.7739 963.7221 804.6098
Lo.80 962.5634 1093.8587 900.4450
Point.Forecast 1202.0745 1339.6925 1113.7263
Hi.80 1441.5856 1585.5263 1379.8025
Hi.95 1568.3752 1715.6629 1546.3794


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [4] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.9      
##  [7] tidytext_0.4.1      DT_0.32             janitor_2.2.0      
## [10] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [13] xts_0.13.2          forecast_8.21.1     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-11          
## [19] NLP_0.2-1           tsibble_1.1.4       lubridate_1.9.3    
## [22] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.2        
## [25] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [28] gsynth_1.2.1        lattice_0.20-45     GGally_2.2.1       
## [31] ggplot2_3.5.0       gridExtra_2.3       plotrix_3.8-4      
## [34] sparklyr_1.8.4      httr_1.4.7          readxl_1.4.3       
## [37] zoo_1.8-12          stringr_1.5.1       stringi_1.8.3      
## [40] data.table_1.15.0   reshape2_1.4.4      fUnitRoots_4021.80 
## [43] plyr_1.8.9          readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.2-0          systemfonts_1.0.5   selectr_0.4-2      
##   [4] lazyeval_0.2.2      websocket_1.4.1     crosstalk_1.2.1    
##   [7] listenv_0.9.1       digest_0.6.34       foreach_1.5.2      
##  [10] htmltools_0.5.7     fansi_1.0.6         ggfortify_0.4.16   
##  [13] magrittr_2.0.3      doParallel_1.0.17   tzdb_0.4.0         
##  [16] globals_0.16.2      vroom_1.6.5         sandwich_3.1-0     
##  [19] askpass_1.2.0       timechange_0.3.0    anytime_0.3.9      
##  [22] tseries_0.10-55     colorspace_2.1-0    xfun_0.42          
##  [25] crayon_1.5.2        jsonlite_1.8.8      iterators_1.0.14   
##  [28] glue_1.7.0          gtable_0.3.4        car_3.1-2          
##  [31] quantmod_0.4.26     abind_1.4-5         mvtnorm_1.2-4      
##  [34] DBI_1.2.2           rngtools_1.5.2      Rcpp_1.0.12        
##  [37] lfe_2.9-0           viridisLite_0.4.2   xtable_1.8-4       
##  [40] bit_4.0.5           Formula_1.2-5       htmlwidgets_1.6.4  
##  [43] timeSeries_4032.109 gplots_3.1.3.1      ellipsis_0.3.2     
##  [46] spatial_7.3-14      farver_2.1.1        pkgconfig_2.0.3    
##  [49] nnet_7.3-16         sass_0.4.8          dbplyr_2.4.0       
##  [52] chromote_0.2.0      utf8_1.2.4          labeling_0.4.3     
##  [55] tidyselect_1.2.0    rlang_1.1.3         later_1.3.2        
##  [58] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [61] cachem_1.0.8        cli_3.6.2           generics_0.1.3     
##  [64] evaluate_0.23       fastmap_1.1.1       yaml_2.3.8         
##  [67] processx_3.8.3      knitr_1.45          bit64_4.0.5        
##  [70] caTools_1.18.2      future_1.33.1       nlme_3.1-153       
##  [73] doRNG_1.8.6         slam_0.1-50         xml2_1.3.6         
##  [76] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.15.0  
##  [79] curl_5.2.0          bslib_0.6.1         highr_0.10         
##  [82] ps_1.7.6            fBasics_4032.96     Matrix_1.6-5       
##  [85] its.analysis_1.6.0  urca_1.3-3          vctrs_0.6.5        
##  [88] pillar_1.9.0        lifecycle_1.0.4     lmtest_0.9-40      
##  [91] jquerylib_0.1.4     bitops_1.0-7        R6_2.5.1           
##  [94] promises_1.2.1      KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [97] parallelly_1.37.0   codetools_0.2-18    ggstats_0.5.1      
## [100] assertthat_0.2.1    boot_1.3-28         gtools_3.9.5       
## [103] MASS_7.3-54         openssl_2.1.1       withr_3.0.0        
## [106] fracdiff_1.5-3      parallel_4.1.2      hms_1.1.3          
## [109] quadprog_1.5-8      timeDate_4032.109   rmarkdown_2.25     
## [112] snakecase_0.11.1    carData_3.0-5       TTR_0.24.4
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))